import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()




class   _UpperCamelCase (		_UpperCAmelCase		,_UpperCAmelCase		,_UpperCAmelCase		,unittest.TestCase					):


			"""simple docstring"""






			__a    :					Dict     					=		StableUnCLIPPipeline
			__a    :					Union[str, Any]     					=		TEXT_TO_IMAGE_PARAMS
			__a    :					List[str]     					=		TEXT_TO_IMAGE_BATCH_PARAMS
			__a    :					List[Any]     					=		TEXT_TO_IMAGE_IMAGE_PARAMS
			__a    :					int     					=		TEXT_TO_IMAGE_IMAGE_PARAMS

			# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
			__a    :					Optional[int]     					=		False





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Tuple:

							'''simple docstring'''


							__lowercase					     =				32
							__lowercase					     =				embedder_hidden_size

							# prior components

							torch.manual_seed(0		)
							__lowercase					     =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

							torch.manual_seed(0		)
							__lowercase					     =				CLIPTextModelWithProjection(
							    CLIPTextConfig(
							        bos_token_id=0 ,  eos_token_id=2 ,  hidden_size=lowerCAmelCase__ ,  projection_dim=lowerCAmelCase__ ,  intermediate_size=37 ,  layer_norm_eps=1E-05 ,  num_attention_heads=4 ,  num_hidden_layers=5 ,  pad_token_id=1 ,  vocab_size=10_00 ,  )		)

							torch.manual_seed(0		)
							__lowercase					     =				PriorTransformer(
							    num_attention_heads=2 ,  attention_head_dim=12 ,  embedding_dim=lowerCAmelCase__ ,  num_layers=1 ,  )

							torch.manual_seed(0		)
							__lowercase					     =				DDPMScheduler(
							    variance_type='''fixed_small_log''' ,  prediction_type='''sample''' ,  num_train_timesteps=10_00 ,  clip_sample=lowerCAmelCase__ ,  clip_sample_range=5.0 ,  beta_schedule='''squaredcos_cap_v2''' ,  )

							# regular denoising components

							torch.manual_seed(0		)
							__lowercase					     =				StableUnCLIPImageNormalizer(embedding_dim=lowerCAmelCase__		)
							__lowercase					     =				DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''		)

							torch.manual_seed(0		)
							__lowercase					     =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

							torch.manual_seed(0		)
							__lowercase					     =				CLIPTextModel(
							    CLIPTextConfig(
							        bos_token_id=0 ,  eos_token_id=2 ,  hidden_size=lowerCAmelCase__ ,  projection_dim=32 ,  intermediate_size=37 ,  layer_norm_eps=1E-05 ,  num_attention_heads=4 ,  num_hidden_layers=5 ,  pad_token_id=1 ,  vocab_size=10_00 ,  )		)

							torch.manual_seed(0		)
							__lowercase					     =				UNetaDConditionModel(
							    sample_size=32 ,  in_channels=4 ,  out_channels=4 ,  down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''') ,  up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''') ,  block_out_channels=(32, 64) ,  attention_head_dim=(2, 4) ,  class_embed_type='''projection''' ,  projection_class_embeddings_input_dim=embedder_projection_dim * 2 ,  cross_attention_dim=lowerCAmelCase__ ,  layers_per_block=1 ,  upcast_attention=lowerCAmelCase__ ,  use_linear_projection=lowerCAmelCase__ ,  )

							torch.manual_seed(0		)
							__lowercase					     =				DDIMScheduler(
							    beta_schedule='''scaled_linear''' ,  beta_start=0.0_0085 ,  beta_end=0.012 ,  prediction_type='''v_prediction''' ,  set_alpha_to_one=lowerCAmelCase__ ,  steps_offset=1 ,  )

							torch.manual_seed(0		)
							__lowercase					     =				AutoencoderKL()

							__lowercase					     =				{
							    # prior components
							    '''prior_tokenizer''': prior_tokenizer,
							    '''prior_text_encoder''': prior_text_encoder,
							    '''prior''': prior,
							    '''prior_scheduler''': prior_scheduler,
							    # image noising components
							    '''image_normalizer''': image_normalizer,
							    '''image_noising_scheduler''': image_noising_scheduler,
							    # regular denoising components
							    '''tokenizer''': tokenizer,
							    '''text_encoder''': text_encoder,
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							}

							return components





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__=0		)	->   Optional[int]:

							'''simple docstring'''


							if str(lowerCAmelCase__		).startswith('''mps'''		):
											__lowercase					     =				torch.manual_seed(lowerCAmelCase__		)
							else:
											__lowercase					     =				torch.Generator(device=lowerCAmelCase__		).manual_seed(lowerCAmelCase__		)
							__lowercase					     =				{
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''prior_num_inference_steps''': 2,
							    '''output_type''': '''numpy''',
							}
							return inputs





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[Any]:

							'''simple docstring'''


							__lowercase					     =				torch_device == '''cpu'''

							self._test_attention_slicing_forward_pass(test_max_difference=lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Tuple:

							'''simple docstring'''


							__lowercase					     =				torch_device in ['''cpu''', '''mps''']

							self._test_inference_batch_single_identical(test_max_difference=lowerCAmelCase__		)




@slow
@require_torch_gpu
class   _UpperCamelCase (		unittest.TestCase					):


			"""simple docstring"""






			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[Any]:

							'''simple docstring'''


							__lowercase					     =				load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'''		)

							__lowercase					     =				StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' ,  torch_dtype=torch.floataa		)
							pipe.to(lowerCAmelCase__		)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__		)
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							__lowercase					     =				torch.Generator(device='''cpu'''		).manual_seed(0		)
							__lowercase					     =				pipe('''anime turle''' ,  generator=lowerCAmelCase__ ,  output_type='''np'''		)

							__lowercase					     =				output.images[0]

							assert image.shape == (7_68, 7_68, 3)

							assert_mean_pixel_difference(lowerCAmelCase__ ,  lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Tuple:

							'''simple docstring'''


							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							__lowercase					     =				StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' ,  torch_dtype=torch.floataa		)
							__lowercase					     =				pipe.to(lowerCAmelCase__		)
							pipe.set_progress_bar_config(disable=lowerCAmelCase__		)
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							__lowercase					     =				pipe(
							    '''anime turtle''' ,  prior_num_inference_steps=2 ,  num_inference_steps=2 ,  output_type='''np''' ,  )

							__lowercase					     =				torch.cuda.max_memory_allocated()
							# make sure that less than 7 GB is allocated
							assert mem_bytes < 7 * 10**9
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	Dict         =    logging.get_logger(__name__)





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				MobileViTConfig()

				# size of the architecture
				if "mobilevit_s" in mobilevit_name:
								__lowercase					     =				[144, 192, 240]
								__lowercase					     =				[16, 32, 64, 96, 128, 160, 640]
				elif "mobilevit_xs" in mobilevit_name:
								__lowercase					     =				[96, 120, 144]
								__lowercase					     =				[16, 32, 48, 64, 80, 96, 384]
				elif "mobilevit_xxs" in mobilevit_name:
								__lowercase					     =				[64, 80, 96]
								__lowercase					     =				[16, 16, 24, 48, 64, 80, 320]
								__lowercase					     =				0.05
								__lowercase					     =				2.0

				if mobilevit_name.startswith('''deeplabv3_'''				):
								__lowercase					     =				512
								__lowercase					     =				16
								__lowercase					     =				21
								__lowercase					     =				'''pascal-voc-id2label.json'''
				else:
								__lowercase					     =				1000
								__lowercase					     =				'''imagenet-1k-id2label.json'''

				__lowercase					     =				'''huggingface/label-files'''
				__lowercase					     =				json.load(open(hf_hub_download(lowercase				,     lowercase				,     repo_type='''dataset'''				)				,     '''r'''				)				)
				__lowercase					     =				{int(lowercase				): v for k, v in idalabel.items()}
				__lowercase					     =				idalabel
				__lowercase					     =				{v: k for k, v in idalabel.items()}

				return config





def 							UpperCAmelCase		(       lowercase				,     lowercase=False				):







				"""simple docstring"""



				for i in range(1				,     6				):
								if F"layer_{i}." in name:
												__lowercase					     =				name.replace(F"layer_{i}."				,     F"encoder.layer.{i - 1}."				)

				if "conv_1." in name:
								__lowercase					     =				name.replace('''conv_1.'''				,     '''conv_stem.'''				)
				if ".block." in name:
								__lowercase					     =				name.replace('''.block.'''				,     '''.'''				)

				if "exp_1x1" in name:
								__lowercase					     =				name.replace('''exp_1x1'''				,     '''expand_1x1'''				)
				if "red_1x1" in name:
								__lowercase					     =				name.replace('''red_1x1'''				,     '''reduce_1x1'''				)
				if ".local_rep.conv_3x3." in name:
								__lowercase					     =				name.replace('''.local_rep.conv_3x3.'''				,     '''.conv_kxk.'''				)
				if ".local_rep.conv_1x1." in name:
								__lowercase					     =				name.replace('''.local_rep.conv_1x1.'''				,     '''.conv_1x1.'''				)
				if ".norm." in name:
								__lowercase					     =				name.replace('''.norm.'''				,     '''.normalization.'''				)
				if ".conv." in name:
								__lowercase					     =				name.replace('''.conv.'''				,     '''.convolution.'''				)
				if ".conv_proj." in name:
								__lowercase					     =				name.replace('''.conv_proj.'''				,     '''.conv_projection.'''				)

				for i in range(0				,     2				):
								for j in range(0				,     4				):
												if F".{i}.{j}." in name:
																__lowercase					     =				name.replace(F".{i}.{j}."				,     F".{i}.layer.{j}."				)

				for i in range(2				,     6				):
								for j in range(0				,     4				):
												if F".{i}.{j}." in name:
																__lowercase					     =				name.replace(F".{i}.{j}."				,     F".{i}."				)
																if "expand_1x1" in name:
																				__lowercase					     =				name.replace('''expand_1x1'''				,     '''downsampling_layer.expand_1x1'''				)
																if "conv_3x3" in name:
																				__lowercase					     =				name.replace('''conv_3x3'''				,     '''downsampling_layer.conv_3x3'''				)
																if "reduce_1x1" in name:
																				__lowercase					     =				name.replace('''reduce_1x1'''				,     '''downsampling_layer.reduce_1x1'''				)

				for i in range(2				,     5				):
								if F".global_rep.{i}.weight" in name:
												__lowercase					     =				name.replace(F".global_rep.{i}.weight"				,     '''.layernorm.weight'''				)
								if F".global_rep.{i}.bias" in name:
												__lowercase					     =				name.replace(F".global_rep.{i}.bias"				,     '''.layernorm.bias'''				)

				if ".global_rep." in name:
								__lowercase					     =				name.replace('''.global_rep.'''				,     '''.transformer.'''				)
				if ".pre_norm_mha.0." in name:
								__lowercase					     =				name.replace('''.pre_norm_mha.0.'''				,     '''.layernorm_before.'''				)
				if ".pre_norm_mha.1.out_proj." in name:
								__lowercase					     =				name.replace('''.pre_norm_mha.1.out_proj.'''				,     '''.attention.output.dense.'''				)
				if ".pre_norm_ffn.0." in name:
								__lowercase					     =				name.replace('''.pre_norm_ffn.0.'''				,     '''.layernorm_after.'''				)
				if ".pre_norm_ffn.1." in name:
								__lowercase					     =				name.replace('''.pre_norm_ffn.1.'''				,     '''.intermediate.dense.'''				)
				if ".pre_norm_ffn.4." in name:
								__lowercase					     =				name.replace('''.pre_norm_ffn.4.'''				,     '''.output.dense.'''				)
				if ".transformer." in name:
								__lowercase					     =				name.replace('''.transformer.'''				,     '''.transformer.layer.'''				)

				if ".aspp_layer." in name:
								__lowercase					     =				name.replace('''.aspp_layer.'''				,     '''.'''				)
				if ".aspp_pool." in name:
								__lowercase					     =				name.replace('''.aspp_pool.'''				,     '''.'''				)
				if "seg_head." in name:
								__lowercase					     =				name.replace('''seg_head.'''				,     '''segmentation_head.'''				)
				if "segmentation_head.classifier.classifier." in name:
								__lowercase					     =				name.replace('''segmentation_head.classifier.classifier.'''				,     '''segmentation_head.classifier.'''				)

				if "classifier.fc." in name:
								__lowercase					     =				name.replace('''classifier.fc.'''				,     '''classifier.'''				)
				elif (not base_model) and ("segmentation_head." not in name):
								__lowercase					     =				'''mobilevit.''' + name

				return name





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase=False				):







				"""simple docstring"""



				if base_model:
								__lowercase					     =				''''''
				else:
								__lowercase					     =				'''mobilevit.'''

				for key in orig_state_dict.copy().keys():
								__lowercase					     =				orig_state_dict.pop(lowercase				)

								if key[:8] == "encoder.":
												__lowercase					     =				key[8:]

								if "qkv" in key:
												__lowercase					     =				key.split('''.'''				)
												__lowercase					     =				int(key_split[0][6:]				) - 1
												__lowercase					     =				int(key_split[3]				)
												__lowercase					     =				model.get_submodule(F"{model_prefix}encoder.layer.{layer_num}"				)
												__lowercase					     =				layer.transformer.layer[transformer_num].attention.attention.all_head_size
												__lowercase					     =				(
												    F"{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."
												)
												if "weight" in key:
																__lowercase					     =				val[:dim, :]
																__lowercase					     =				val[dim : dim * 2, :]
																__lowercase					     =				val[-dim:, :]
												else:
																__lowercase					     =				val[:dim]
																__lowercase					     =				val[dim : dim * 2]
																__lowercase					     =				val[-dim:]
								else:
												__lowercase					     =				val

				return orig_state_dict





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__lowercase					     =				Image.open(requests.get(lowercase				,     stream=lowercase				).raw				)
				return im





@torch.no_grad()
def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				,     lowercase=False				):







				"""simple docstring"""



				__lowercase					     =				get_mobilevit_config(lowercase				)

				# load original state_dict
				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)

				# load 🤗 model
				if mobilevit_name.startswith('''deeplabv3_'''				):
								__lowercase					     =				MobileViTForSemanticSegmentation(lowercase				).eval()
				else:
								__lowercase					     =				MobileViTForImageClassification(lowercase				).eval()

				__lowercase					     =				convert_state_dict(lowercase				,     lowercase				)
				model.load_state_dict(lowercase				)

				# Check outputs on an image, prepared by MobileViTImageProcessor
				__lowercase					     =				MobileViTImageProcessor(crop_size=config.image_size				,     size=config.image_size + 32				)
				__lowercase					     =				image_processor(images=prepare_img()				,     return_tensors='''pt'''				)
				__lowercase					     =				model(**lowercase				)
				__lowercase					     =				outputs.logits

				if mobilevit_name.startswith('''deeplabv3_'''				):
								assert logits.shape == (1, 21, 32, 32)

								if mobilevit_name == "deeplabv3_mobilevit_s":
												__lowercase					     =				torch.tensor(
												    [
												        [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
												        [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
												        [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
												    ]				)
								elif mobilevit_name == "deeplabv3_mobilevit_xs":
												__lowercase					     =				torch.tensor(
												    [
												        [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
												        [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
												        [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
												    ]				)
								elif mobilevit_name == "deeplabv3_mobilevit_xxs":
												__lowercase					     =				torch.tensor(
												    [
												        [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
												        [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
												        [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
												    ]				)
								else:
												raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}"				)

								assert torch.allclose(logits[0, :3, :3, :3]				,     lowercase				,     atol=1E-4				)
				else:
								assert logits.shape == (1, 1000)

								if mobilevit_name == "mobilevit_s":
												__lowercase					     =				torch.tensor([-0.9866, 0.2392, -1.1241]				)
								elif mobilevit_name == "mobilevit_xs":
												__lowercase					     =				torch.tensor([-2.4761, -0.9399, -1.9587]				)
								elif mobilevit_name == "mobilevit_xxs":
												__lowercase					     =				torch.tensor([-1.9364, -1.2327, -0.4653]				)
								else:
												raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}"				)

								assert torch.allclose(logits[0, :3]				,     lowercase				,     atol=1E-4				)

				Path(lowercase				).mkdir(exist_ok=lowercase				)
				print(F"Saving model {mobilevit_name} to {pytorch_dump_folder_path}"				)
				model.save_pretrained(lowercase				)
				print(F"Saving image processor to {pytorch_dump_folder_path}"				)
				image_processor.save_pretrained(lowercase				)

				if push_to_hub:
								__lowercase					     =				{
								    '''mobilevit_s''': '''mobilevit-small''',
								    '''mobilevit_xs''': '''mobilevit-x-small''',
								    '''mobilevit_xxs''': '''mobilevit-xx-small''',
								    '''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
								    '''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
								    '''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
								}

								print('''Pushing to the hub...'''				)
								__lowercase					     =				model_mapping[mobilevit_name]
								image_processor.push_to_hub(lowercase				,     organization='''apple'''				)
								model.push_to_hub(lowercase				,     organization='''apple'''				)


if __name__ == "__main__":
		__a    :	int         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--mobilevit_name""",
		    default="""mobilevit_s""",
		    type=str,
		    help=(
		        """Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
		        """ 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
		    ),
		)
		parser.add_argument(
		    """--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
		)
		parser.add_argument(
		    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
		)

		__a    :	Dict         =    parser.parse_args()
		convert_movilevit_checkpoint(
		    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)